Importance of Predictive Analytics: What it is & why it’s Important?

Why Do We Need Predictive Analytics?

For the uninitiated, Predictive Analytics is a term that describes using the past data to develop informed guesses about future outcomes. Many business corporations have been using this advanced tool for many years to assess risk and detect frauds. In fact, many marketing companies have used their predictive analytical skills to predict the demand for their products or services, personalize their content and increase conversion. Netflix is a classic example of predictive analytics that you come across in everyday life. If you ever wondered how Netflix recommended just the right shows and movies for you, you have the answer now.

In this article, we will discuss how predictive analytics is increasingly being welcomed in many industries and how important is it.

Optimize Marketing Productivity: Using their predictive analytical skills, marketers can foresee the trends and outliers to take better decisions. They are empowered to identify prospective customers who are likely to buy or have the highest propensity to buy their products or services. This can further provide the marketers with an advantage of optimizing their campaigns and generating better returns on investments.

Gain a Competitive Advantage: Companies can make their way to the top by using predictive analytical skills to develop intent-based personalization. By creating effective predictive models based on the company’s strengths and competitor’s weaknesses, you can innovate and outshine your competitors.

Understand Your Customers Better: With a reliable predictive analytics model in place, your company would be able to analyze all the structured as well as the unstructured data and predict customer expectations. Be it the geographic and demographic data or the specific inputs of the prospective customers from their social media, it would be possible for you to identify customers who can convert and get more business for you.

Identify Areas of Attrition: Using your predictive analytical skills, you can forecast the next probable action of your customers and win back the lost customers. You can identify the reasons why your earlier customers switched to your competitors and model out others who are planning to exit. Since you know this at a very initial stage, you can invest your time in planning strategies aimed at these customers to retain them and build long-term relationships.

Identify New Revenue Opportunities: Predictive models can obtain rare insights related to the customers. Companies can analyze the buying patterns of their customers and link them with promotional offers and discounts to create new revenue sources. Using an identity management system, you can collect valuable data on your customers like their location, IP address, number of logins and the timestamp of their logins, all of which will help you to figure out user behaviour and boost your revenue.

What is Predictive Analytics Used For?

Ever since predictive analytics has been taking huge strides in supporting companies move into uncharted territories, businesses don’t have to just blindly trust their gut feeling anymore. They can make data-driven decisions after considering market conditions, insights into their customers and more.
Let’s look at the top use cases for predictive analytics across different industry verticals.

Energy & Utilities: The importance of predictive analytics in the energy sector is hardly a secret. It is used to predict the demand and supply of electrical energy through the power grids. By using complex models to study the plant availability, impact of changing weather pattern and other such factors, the energy industry can save valuable resources.

Banking and Financial Services: Banks and other financial institutions are deploying predictive analytics to ensure that their clients can enjoy a superlative experience that is secure and user-friendly. Such models can customize products and services based on the clients’ profile, identify opportunities for cross-selling and detect frauds among other benefits.

Manufacturing: By combining the benefits of business analytics with predictive analytics techniques, the manufacturing industry can streamline all their multiple processes right from the supply chain management to the distribution and improve their quality of service.

What are the Outcomes of Predictive Analytics?

Using a company’s historical data, predictive analytics algorithms combined with machine learning and artificial intelligence to forecast what will happen in the future. When a mathematical model is used to study such historical data that has been fed into the system, the outcome can lead to positive operational changes. These could mean reduced risks, lesser malpractices or incidences of frauds, an increase in revenue, improved conversions and more.

How Does Predictive Analytics Work?

The following steps should form the core of every efficient predictive analytics project:

Identify Business Outcomes: First, identify the questions you need an answer to. What will be the business decisions you need to make depending on the insights gained from the answers to these questions? The knowledge of this is critical in using predictive analysis.

Determine Data Required to Train: Next comes the data being captured by your system. It should be sufficient and clean enough so that your predictive models can be accurately trained. If the data is insufficient to identify any predictive patterns, then it can directly impact your outcome.

Training Your System: By using different predictive analytic techniques like statistical analysis, data mining, neural networks and machine learning, train your system to learn from the historical data of your company. The predictive model should be able to identify trends and user behaviours and correlate your data into successful predictions.

Validate Your Results: By working closely with business analysts, ensure that your predictive models make business sense because wrong or inaccurate predictive analytic algorithms can seriously harm your business with untrue predictions.

Use the Insights: Predictive analytics is a continuous process. Always retrain and test the models to improve your results and then embed the valuable insights into your line of business applications.

What is Predictive Analytics Data Mining?

Data Mining is a crucial step in the process of predictive analytics. It is used to extract useful information from the current data (usually large data sets) to predict trends. It aids the analytics team by finding the relevant data to analyze and be used in the predictive models to find what will happen later on in the business.
Example of Predictive Analytics using Data Mining
A hotel chain interested in knowing the number of customers that can be expected in a particular location over the weekend based on their past data so they can arrange for the required staff and resources.

How Do I Start Predictive Analytics?

While getting started with predictive analytics is not so easy, when done right, it can prove extremely beneficial to companies that are religiously invested and committed to the project. The ideal approach would be, to begin with, a limited-scale pilot project in a critical business area. This way, you would be able to limit the start-up costs and reduce the time frame before which the money can start pouring in. Once the predictive model is successful, it will only require fine-tuning to grind out actionable insights in the years to come.

Is Forecasting Predictive Analytics?

Through forecasting, companies can find out about the different trends that are likely to dominate the market in future. On the other hand, the predictive analysis uses a mathematical model to leverage these statistics to predict future outcomes for the business.

What is Predictive Analytics in Big Data?

Predictive analytics is the practical result of Big Data and Business Intelligence. Big data has revolutionized the way in which companies can now leverage their data and use it to their advantage. It has made it possible for line-managers to use non-transactional data to make strategic decisions. Earlier, only data scientists and statisticians who had the required mathematical skills could understand the predictive analytics technique, but big data has made it easier to capture and store massive volumes of data for faster analysis.

Is Predictive Analytics Machine Learning?

Machine learning and predictive analytics are both centred on efficient data processing. Machine learning can be taken as an extension of predictive analytics that is responsible for identifying patterns and self-learning. The main difference between both these technologies is that while machine learning does not rely on human experts to test the associations between cause and outcome, predictive analytics needs human intervention. Also, predictive analytics must be refreshed with “change” data whereas machine learning can automatically recalibrate new data into the model based on real-time.

What is the Difference between Prediction and Forecasting?

Forecasting can be said to be a subset of prediction or predictive modelling. This means all forecasts are predictions, but not all predictions are forecasts. While forecasts take into consideration time series analysis or probabilities, prediction includes statistical theories.

Conclusion

Predictive analytics is a smart way to add more insight and clarity into your business decisions. Even though it may take a lot of time to collect useful data and devise a plan to sort through it, when you see the results of what it can do, it will be worth it.

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Utmost care has been taken to ensure that there is no copyright violation or infringement in any of our content. Still, in case you feel that there is any copyright violation of any kind please send a mail to abuse@edupristine.com and we will rectify it.

Disclaimer

GARP does not endorse, promote, review or warrant the accuracy of the products or services offered by EduPristine of GARP Exam related information, nor does it endorse any pass rates that may be claimed by the Exam Prep Provider. Further, GARP is not responsible for any fees or costs paid by the user to EduPristine nor is GARP responsible for any fees or costs of any person or entity providing any services to EduPristine. ERP®, FRM®, GARP® and Global Association of Risk Professionals™ are trademarks owned by the Global Association of Risk Professionals, Inc.CFA® Institute does not endorse, promote, or warrant the accuracy or quality of the products or services offered by EduPristine.

CFA® Institute, CFA® Program, CFA® Institute Investment Foundations™ and Chartered Financial Analyst® are trademarks owned by CFA® Institute. Utmost care has been taken to ensure that there is no copyright violation or infringement in any of our content.

Still, in case you feel that there is any copyright violation of any kind please send a mail to abuse@edupristine.com and we will rectify it.